Vagrancy conditions in spring 2022 have been moderate, and are expected to decrease over the next 14 days. Vagrancy conditions are determined here by the factors that influence a bird’s ability to use the Earth’s geomagnetic field to navigate during migration.
The chance to see a rare bird is also determined by seasonality. Here, the relative number of species for this week is indicated by color scale, and +, - and = signs indicate where richness is increasing, decreasing, or staying about the same.
{r, fig.width=10, fig.height=7,fig.align='center',fig.cap="Vagrancy varies year-to-year for each species. Here is the trend for one random species (updated daily)."} # model_in <- readRDS("~/Documents/Coding/R/Pop_Disp_Vagrancy/model_fits/M3_spring_2022_05_30/M3_spring_2022_05_30.rds") # model_df <- readRDS("~/Documents/Coding/R/Pop_Disp_Vagrancy/model_fits/M1_2_3_data_spring_2022_05_30.rds") # # model_params <- as.data.frame(MCMCchains(model_in,params=c("alpha","theta","psi","beta1", # "beta2","mu_beta1","mu_beta2", # "mu_psi","phi"))) # spec_names <- unique(model_df$spec_name) # # Pick species # # spec_num <- sample(1:length(spec_names),1) # # spec_data <- filter(model_df,spec_name == spec_names[spec_num]) # year_pred_rec <- c() # for (n in 1:nrow(spec_data)){ # #Reference # #lp = exp(alpha[ii]+theta*eff+ psi[ii].*xx + beta1[ii].*xx1 + beta2[ii].*xx2); # #yy ~ neg_binomial_2(lp,phi); # # year <- spec_data$Year[n] # eff <- spec_data$total_count_stand[n] # xx <- spec_data$xx[n] # xx1 <- spec_data$xx1[n] # xx2 <- spec_data$xx2[n] # sz <- as.numeric(unlist(model_params["phi"])) # lp <- as.numeric(unlist(model_params[paste("alpha[",spec_num,"]",sep="")])) + # as.numeric(unlist(model_params[paste("theta",sep="")])) * eff + # as.numeric(unlist(model_params[paste("psi[",spec_num,"]",sep="")])) * xx + # as.numeric(unlist(model_params[paste("beta1[",spec_num,"]",sep="")])) * xx1 + # as.numeric(unlist(model_params[paste("beta2[",spec_num,"]",sep="")])) * xx2 # pois_draw <- mean(qnbinom(p = .5,size = sz,mu = exp(lp))) # lower1 <- mean(qnbinom(p = .05,size = sz,mu = exp(lp))) # upper1 <- mean(qnbinom(p = .95,size = sz,mu = exp(lp))) # # # # year_pred <- c(year,pois_draw,lower1,upper1) # # year_pred_rec <- rbind(year_pred_rec,year_pred) # } # year_pred_rec <- as.data.frame(year_pred_rec) # plot(year_pred_rec$V1,year_pred_rec$V2,cex=0.1,col="black",pch=19,ylim=c(0,(max(year_pred_rec$V2)*1.25)), # xlab="Year",ylab="Predicted Vagrancy",main=spec_names[spec_num]) # # points(year_pred_rec$V1,year_pred_rec$V2,type="l",lwd=4,col="dodgerblue4",xlab="Year",ylab="# Vagrants",main=spec_names[spec_num]) #Current vagrancy conditions
Current magnetic field distortion
Current solar activity conditions
This dashboard is designed to be used as a tool for US birders and twitchers to evaluate the likelihood of seeing a rare bird. This tool integrates three known factors influencing vagrancy - current space weather conditions, seasonality, and demography - to predict the likelihood of seeing a rare bird. Please send questions/comments/suggestions to rarebirdforecast@gmail.com